# Staff Engineer – Experimentation Team

**Company:** [Launchdarkly](https://hotfix.jobs/companies/launchdarkly)
**Location:** Remote
**Role:** Backend Engineering
**Salary:** $183k – $295k/yr
**Experience:** 10+ years
**Skills:** Go, Python, Snowflake, Databricks, Redshift, BigQuery, AWS, GCP, Hypothesis Testing, Sequential Analysis, Cuped, Contextual Bandits, Bayesian Optimization, Event-Driven Architectures, Data Pipelines
**Posted:** 2026-04-29

> Builds statistical engines and warehouse-native analysis pipelines for A/B testing and adaptive experimentation (contextual bandits). Requires 10+ years experience, deep applied statistics/ML, backend expertise in Go/Python, and warehouse integration across Snowflake, Databricks, etc.

## Job Description

## Responsibilities
- Build the experimentation statistical engine — hypothesis testing, sequential analysis, variance reduction (CUPED, Winsorization), power analysis. Ensure statistical correctness across all experiment types.
- Design warehouse-native experimentation that runs analysis inside customer warehouses (Snowflake, Databricks, Redshift, BigQuery). Build modular, warehouse-agnostic abstractions for rapid new backend support.
- Lead adaptive experimentation — contextual bandit systems, Bayesian optimization, automated allocation beyond simple A/B tests.
- Drive the platform roadmap with product, design, and data science. Shape what we build, not just how.
- Collaborate cross-functionally with Warehouse Integrations, SDK, Platform, and Data Science teams.
- Mentor engineers and raise the team's bar for statistical rigor and system design.
- Own operational excellence — monitoring, observability, incident response, on-call. Robust telemetry and alerting.

## Qualifications
- 10+ years building large-scale experimentation platforms, statistical analysis systems, or data-intensive backend services.
- Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.
- Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL-based allocation.
- Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar.
- Expertise in Go, Python, or similar for backend services and statistical computation.
- Experience with event-driven architectures, data pipelines, and large-scale data processing.
- Cloud environments (AWS, GCP) with infrastructure-as-code.
- Technical leadership: setting direction, breaking down complex problems, influencing across teams.
- Ability to translate statistical concepts for product and engineering audiences.

## Pay
Target pay ranges based on Geographic Zones for Level 5:  
**Zone 1** (San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle): $214,800 - $295,350  
**Zone 2** (Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago): $193,400 - $265,870  
**Zone 3** (All other US locations): $182,600 - $251,020  
Exact compensation may vary based on skills, experience, and location. Includes RSUs, health, vision, dental insurance, and mental health benefits.

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**Canonical:** https://hotfix.jobs/jobs/e0f4bb1c-751a-4c5a-9045-3f3276e2390e